Bibliographic record
Abstract
1. The Letter of Credit and its Legal Framework. The Nature of Letters of Credit. The Legal Framework of Letters of Credit. Summary. 2. The Fraud Rule: An Overview. Meaning and Rationale. Historical Development. The Fraud Rule in the United States. The Fraud Rule in Other Jurisdictions. The Fraud Rule in ICC Rules. The Fraud Rule under the UNCITRAL Convention. Summary. 3. The Standard of Fraud. The Position in the US. The Position in the UK. The Position in Canada. The Position in Australia. The Position under the UNCITRAL Convention. Summary. 4. The Locus of Fraud. The Controversy. Case Studies. The Solution. Commentary. 5. Identity of the Fraudulent Party. Statutory Provisions. Applicant Fraud. Third Party Fraud: The Case of United City Merchants. Summary. 6. Presenters Immune from the Fraud Rule. Preliminary Observations. Likely Presenters. Conclusion. 7. The Fraud Rule in the People's Republic of China. The Legal System of the PRC. The Fraud Rule in the PRC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".